Introduction

Over the past few years, Major League Baseball has seen substantial shifts in what have traditionally been considered dominant pitches. Mets ace Justin Verlander remarked earlier this year that sliders haven’t been performing as well as in the past, specifically, OPS on sliders is “the highest it’s been in the pitch tracking years”1. The introduction of the sweeper, an attempt to clarify and designate differences between two types of sliders, and new evaluation metrics such as Stuff+, Location+, and Pitching+, have only added dimension to the evaluation of specific pitch types and are indicative of a growing focus on individual pitch characteristics. While the discrete qualities of a pitch (such as vertical / horizontal movement, velocity, and spin rate) are undoubtedly useful in evaluating and explaining elite pitchers, the effects that sequencing and in-game pitching strategy have on outcomes cannot be understated. Therefore, integrating inter-pitch dynamics with discrete pitch characteristics might more accurately model a pitcher’s effectiveness. Fangraph’s Stuff+ metric does a satisfactory job in evaluating the efficacy of a pitch based on discrete characteristics, but it is limited in that it fails to consider the art of pitch sequencing. Our motivation behind this project is to assess pitch effectiveness and consider how inter-pitch dynamics can enhance existing evaluation metrics.



Data

Exploratory Data Analysis (EDA)

Note Recategorization:
“Sliders” includes Slurves and Sweepers
“Curveballs” includes Knuckle Curves
“Changeup” includes Splitters


Pitch Horizontal Vertical Pitch Proportion Spin Rate Speed
4-Seamer 7.45 14.86 0.46 2285.29 93.93
Sinker 15.00 22.89 0.39 2127.16 93.21
Cutter 2.88 25.97 0.34 2380.57 88.97
Splitter 11.71 33.09 0.28 1459.77 86.37
Slider 6.42 36.28 0.34 2432.44 84.94
Changeup 14.03 32.27 0.26 1754.87 84.59
Curveball 9.45 53.35 0.26 2572.18 79.64

Variable Importance Plots

Sinkers, Cutters, and Four-Seam Fastballs have been aggregated into a singular “Fastball” category.

wOBA

Fastballs

Sliders

Curveballs

Change-Ups

xwOBA

Fastballs

Sliders

Curveballs

Change-Ups

Run Value / 100

Fastballs

Sliders

Curveballs

Change-Ups

Pitch-by-Pitch Breakdowns:


Intra-Pitch Data Analysis

Decision Trees

wOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

xwOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Run Value / 100

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Pitch Characteristics

wOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

xwOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Run Value / 100

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Pitch Movement

wOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

xwOBA

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Run Value / 100

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Intra-Pitch xwOBA GAM

Four-Seam

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Inter-Pitch Data Analysis

Inter-Pitch xwOBA Models

GAM

Four-Seams

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Random Forest

Four-Seams

Cutters

Sinkers

Sliders

Curveballs

Change-Ups

Comparative

Model Accuracy Comparison

Intra-Pitch GAM

Inter-Pitch GAM

Inter-Pitch Random Forest

RMSE Comparison

Acknowledgements

We would like to thank Meg Ellingwood and Shamindra Shrotriya, the leaders of the Carnegie Mellon Summer Undergraduate Research Experience in Statistics, for their invaluable knowledge, guidance, and instruction throughout this research experience. This project would not have been possible without the help of Dr. Ron Yurko and Sean Ahmed, Pirates Director of R&D. We would like to thank the entire Carnegie Mellon Sports Analytics Camp teaching team for their support and guidance during this project.

Citations

[1] Sammon, W., & Sarris, E. (2023, July 7). Fall of the slider: Why are hitters feasting on MLB’s once-deadly breaking ball? The Athletic. https://theathletic.com/4671150/2023/07/07/mlb-sliders-hitters-success/

  1. Ethan Park, University of Southern California, ↩︎

  2. Evan Wu, Elon University, ↩︎

  3. Priyanka Kaul, Harvard University, ↩︎